基于扩样技术和地理加权泊松回归模型的交通量估计

提出了扩样和地理加权泊松回归(GWPR)相结合的方法来估计有限观测值下的路网流量。首先,采用基于空间相似性的扩样方法对不平衡的观测流量进行纠正;然后,考虑道路几何特征和建成环境等因素的影响,采用地理加权泊松模型估计车道的小时交通量。结果表明,与传统线性回归模型和原始样本下的地理加权泊松模型相比,组合模型具有最佳的估计性能。此外,自变量与交通量关系的局部空间异质性也得到了很好的捕捉。

Estimating Traffic Volume Based on Sampling Expansion Technique and Geographically Weighted Poisson Regression

A method combining sampling expansion with geographically weighted Poisson regression (GWPR) was proposed to estimate the road network traffic volume with limited observation values. Firstly, a sampling expansion method based on the spatial similarity was employed to correct the imbalance missing data. Then, the GWPR was employed to estimate the hourly traffic volume of the lane considering the influence of the geometric characteristics of the road and the built environment. Results show that: compared with traditional linear models and GWPR with the original sample set, the proposed combination model has the best estimation performance. In addition, the local spatial heterogeneity of the relationship between independent variables and traffic volume is also well captured.